The spectral efficiency and connectivity optimization of NOMA always leads to nonconvex resource allocation problems, which were solved by conventional algorithms such as successive convex approximation and matching theory with high complexity and impractical implementation [1]}.
Therefore, there is a need to introduce new distributed learning techniques that can be used to address a variety of resource management challenges such as distributed power control for multi-cell NOMA [2]}, joint user association and beamforming design [3]}, and dynamic user clustering [4]}.
For multi-cell power control, FRL enables each BS to build the relationship between the power control schemes and utility values so as to find the optimal power control scheme.
FRL can also be used to study the user association and beamforming of a multi-antenna NOMA network [5]}.
Further, the use of FRL for dynamic user clustering in NOMA, where users individually learn the clustering parameters by RL and the BS builds the unified clustering parameters based on the received clustering parameters from all users.
